The improved generalisation capability of ensembles, compared to their constituent classifiers (also known as the base classifiers), has long been established theoretically and experimentally. This capability is attributed to the complementarity of the individual classifiers in an ensemble, which jointly offer an error correcting mechanism, and is manifested in low prediction bias and variance [1]}, [2]}, [3]}, [4]}.
The combination rules employed by these systems can be as simple as taking a vote between the base classifiers, or more complex, where the classifiers are trained to compensate for the weaknesses of each other.
